1. A NDROID A UTOMATIC OBJECT DETECTION CV Project – 2014 By Ohad Zadok.

2. I NTRODUCTION Motivation Automatic Object Detection can be used to detect small objects in a photo and classify them. It can have security usages when trying to detect enemy units in a satellite images or weapons in security cameras. It can also be used to determine the context in which an image is taken by the objects in it.

3. I NTRODUCTION Project Goal The project goal is to successfully identify objects from a given set after training a model.

4. A PPROACH AND M ETHOD Extracting Descriptors of an image to a vector of descriptors. Creating an SVM model from the set of images. Taking a photo of the new, unidentified object. creating again the same vector of descriptors and run the model on the vector. More on that at the Doc.

5. Algorithms GrabCut http://grabcut.weebly.com/uploads/1/3/7/5/13757421/2018613.jpg?1351369992

6. Algorithms GrabCut BeforeAfter

7. Algorithms SVM http://www.thebookmyproject.com/cars/intrusion-detection-technique-using-k-means-fuzzy-neural-network-svm-classifiers/

8. I MPLEMENTATION Android System. Photos from device's native camera. A vector of descriptors was than calculated using opencv extension. SVM model was created using LibSVM library. The Entire project is run on a device with no need for external resources.

9. I MPLEMENTATION

11. C ONCLUSION When applying the system to relatively small number of training sets the results are pretty accurate. When we apply it to a bigger set of objects it become unreliable, Future versions of the system can try to use different descriptors such as contours and get even more reliable results.

12. R EFERENCES http://www.csie.ntu.edu.tw/~cjlin/libsvm/ https://en.wikipedia.org/wiki/Support_vector_machine http://www.cs.ru.ac.za/research/g02m1682/